High speed data communication between physically separated integrated components is a common function in current communication infrastructure systems. The separated integrated circuits can be physically separated on a common circuit board, or on separate circuit boards connected by a backplane, cable, or other communication medium.
As the data rate increases, as the physical separation distance increases, and/or the quality of the communication medium decreases, the transmitted signal will be increasingly impaired by a process commonly called Inter-Symbol Interference (ISI). ISI effectively smears the transmitted pulse waveform such that, at the receiver, each received pulse contains portions of signal energy from previously transmitted symbols (called Post-Cursor ISI) and may even contain portions of signal energy from symbols yet to be received (called Pre-Cursor ISI).
The phenomenon of ISI is well understood by those knowledgeable in the art. It is also well known that the impairment process of ISI can largely be mitigated through the use of equalizers, whether they are analog equalizers, digital equalizers, or combination of both. Within the scope of digital equalization, the equalizer can be in the form of a Finite Impulse Response (FIR), Infinite Impulse Response (IIR), Decision Feedback Equalizer (DFE), among others. Within the scope of analog equalization, the equalizer can be in the form of a Continuous Time Linear Equalizer (CTLE), Analog FIR, among others.
Often, a complicating issue is that the characteristic response of the transmission channel is unknown and can change from one installation to another. In these cases, a calibration, or adaptation process must be implemented to determine the required configuration of the receive equalizer that will exhibit the least ISI.
One type of common receiver architecture has the receiver sampling the received signal at a frequency that is twice the baud rate of the received signal. Using timing recovery strategies of known art, such as, for example, the Gardner method (F. M. Gardner, “A BPSK/QPSK Timing—Error Detector for Sampled Receivers”, IEEE Transactions on Communications, vol. COM-34, No. 5, May 1986), the sampling of the received waveform is adjusted such that one set of alternating samples aligns with the center of the received symbol waveform pulse with the opposite alternating samples aligning with the point mid-way between consecutive symbol pulses. FIG. 1 depicts such a common, prior art receiver architecture including EQ adaptation.
Shown in FIG. 1 is an analog signal, or received analog signal 30, that contains bit sequences having a baud rate (symbol rate). The received analog signal is sampled, at twice the baud rate, and digitized by an analog to digital converter (ADC) 32. The digitized output 33 of the ADC 32 is provided to a finite impulse response equalizer (FIR EQ) 34. The FIR EQ 34 modifies (controls) the digitized output 33 and provides, at the output of the FIR EQ 34 an equalized signal 35. The equalized signal 35 is provided to a Serial In/Parallel Out (SIPO) device 36, which can also be called as serializer/deserializer device. The SIPO device 36 outputs a data signal 37 and a timing signal 38, each being a digital signal and each having the nominal baud rate to the received analog signal 30.
The timing digital signal 38 and the data signal 37 are provided to a timing error detector (TED) 39, which controls the timing of a sample clock 40; the sample clock 40 controls the ADC 32. With respect to the data signal 37, it is processed by a slicer 42, whose output symbol signal 48 can be Boolean values “0”, or “1”, or, can be the constellation equivalent of the Boolean values, i.e., a “−1” or a “+1”. A summation device 44 takes the difference between the output symbol signal 48 and the data signal 37. The difference 45 is provided to a controller (or adapter) 46, which controls the FIR EQ 34 as a function of the difference 45, and as a function of the digitized output 33.
FIG. 2 depicts an exemplary “eye diagram” of the received waveform and the location of the waveform sampling instances as provided by the timing recovery mechanism of FIG. 1. A data sample DSn is obtained by setting the sample clock 40 such that the middle of the eye in the eye diagram of FIG. 2 is sampled by the ACD 32. Timing samples TSn−1 and TSn are obtained by sampling midway between adjacent eyes The ADC 32 can have many threshold levels (e.g., 32 levels as provided by a 5-bit ADC), or can have fewer levels (e.g., 2-levels as provided by a 1-bit slicer).
FIG. 3 shows the same eye diagram as in FIG. 2 but superimposed with boundaries 50 defining areas within which signals samples are likely to be present/measured. FIG. 4 shows only the boundaries 50 of FIG. 3.
A common means of adapting a digital FIR receiver equalizer is to use the Least Mean Square (LMS) algorithm to minimize the signal variation of the signal samples centered in the middle of the eye. Those knowledgeable in the art will recognize that the LMS algorithm is a simple to implement method of approximating the Minimum Mean Square Estimate (MMSE) which minimizes the combination of ISI and noise power at the Baud Sample.
While the LMS algorithm applied to eye-centered samples is a commonly used approach to receiver EQ adaptation, and one that optimizes the vertical eye opening at the location of the center eye sample, it is known to be sub-optimal with respect to the horizontal eye opening (for example, see A. C. Carusone, “An Equalizer Adaptation Algorithm to Reduce Jitter in Binary Receivers”, IEEE Transactions on Circuits and Systems—II: Express Briefs, Vol. 53, No. 9, September 2006). For example, the exemplary eye diagram of FIG. 4 could be transformed, by an LMS algorithm applied to eye-centered samples, into the eye diagram of FIG. 5, which has a diminished horizontal opening. Those knowledgeable in the art will recognize that the vertical eye has been improved due to the reduction in ISI at the center of the eye, but the horizontal eye opening has been compromised (reduced), and hence, tolerance to sinusoidal jitter has been reduced. The reduction in ISI is shown in FIG. 5 at reference numeral 52 and the reduced horizontal opening is shown at reference numeral 54.
To mitigate this problem, a common technique in current art is to adapt the receiver EQ (e.g., the FIR EQ 34 in FIG. 1) with the purpose of minimizing the amplitude variation at the zero-crossing between symbols of opposite polarity. Such zero-crossing areas are shown in FIG. 6, at reference numeral 56. While this typically improves horizontal eye opening relative to that provided by normal eye center LMS adaptation, it has been found that zero-crossing adaptation is not reliable when the received signal is impaired by strong sinusoidal jitter or transient timing offset due to other impairments such as spread spectrum frequency modulation.
Therefore, improvements in receiver equalization are desirable.